{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "lines = []\n",
    "\n",
    "with open('titanic_train.csv') as csv_file:\n",
    "    csv_reader = csv.reader(csv_file, delimiter=',')\n",
    "    for line in csv_reader:\n",
    "        lines.append(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = lines[1:]\n",
    "passengers = []\n",
    "headers = lines[0]\n",
    "\n",
    "for d in data:\n",
    "    p = {}\n",
    "    for i in range(0,len(headers)):\n",
    "        key = headers[i]\n",
    "        value = d[i]\n",
    "        p[key] = value\n",
    "    passengers.append(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "survived = [p['Survived'] for p in passengers]\n",
    "pclass = [p['Pclass'] for p in passengers]\n",
    "age = [float(p['Age']) for p in passengers if p['Age'] != '']\n",
    "gender_survived = [p['Sex'] for p in passengers if int(p['Survived']) == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "plt.title(\"Survived\")\n",
    "plt.pie(Counter(survived).values(), labels=Counter(survived).keys(), autopct='%1.1f%%', \n",
    "        colors=['lightblue', 'lightgreen'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"surviving passengers count by gender\")\n",
    "plt.bar(Counter(gender_survived).keys(), Counter(gender_survived).values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
